We present CrossLift, a technique for computing cross fields on meshes guided by visual features in images. We leverage powerful text-to-image priors that are capable of synthesizing images of feature-aligned quad meshes in 2D. We extract this signal as explicit per-pixel directions in the 2D images, which we then back-project to the mesh surface. We aggregate these candidate surface directions by performing two smooth interpolations on the mesh surface (first within each view and second across multiple views). We propose custom confidence-based weights for the candidate directions in each interpolation that allow us to resolve conflicts between candidates on the same face and smoothly interpolate our field to occluded faces. Our method is modular and can be used with many different 2D visual priors. We show additional applications to texture-aligned quad meshing as well as interactive cross-field design using coarse, user-drawn lines as signal. We demonstrate the effectiveness of CrossLift on a diverse set of both organic and mechanical shapes and produce quad meshes that exhibit superior semantic alignment as compared to existing methods. Project page at: https://crosslift.github.io/

翻译：我们提出CrossLift技术，一种通过图像视觉特征引导网格表面交叉场计算的方法。该方法利用强大的文本到图像先验，能够合成具有特征对齐特性的二维四边形网格图像。我们从2D图像中提取显式逐像素方向信号，并将其反向投影到网格表面。通过执行两次网格表面平滑插值（首先在单视图内，其次跨多视图），聚合这些候选表面方向。我们针对每次插值中的候选方向提出基于置信度的自定义权重，以解决同一面片内候选方向的冲突问题，并平滑插值至被遮挡面片。该方法具有模块化特性，可与多种2D视觉先验结合使用。我们进一步展示了该方法在纹理对齐四边形网格生成中的应用，以及利用用户粗略手绘线条作为信号进行交互式交叉场设计。通过在多样化的有机与机械形状上的实验验证，CrossLift生成的四边形网格相比现有方法展现出更优的语义对齐效果。项目主页：https://crosslift.github.io/